In this study we examined changes in the large-scale structure of resting-state brain networks in patients with Alzheimer's disease compared with non-demented controls, using concepts from graph theory. Magneto-encephalograms (MEG) were recorded in 18 Alzheimer's disease patients and 18 non-demented control subjects in a no-task, eyes-closed condition. For the main frequency bands, synchronization between all pairs of MEG channels was assessed using a phase lag index (PLI, a synchronization measure insensitive to volume conduction). PLI-weighted connectivity networks were calculated, and characterized by a mean clustering coefficient and path length. Alzheimer's disease patients showed a decrease of mean PLI in the lower alpha and beta band. In the lower alpha band, the clustering coefficient and path length were both decreased in Alzheimer's disease patients. Network changes in the lower alpha band were better explained by a 'Targeted Attack' model than by a 'Random Failure' model. Thus, Alzheimer's disease patients display a loss of resting-state functional connectivity in lower alpha and beta bands even when a measure insensitive to volume conduction effects is used. Moreover, the large-scale structure of lower alpha band functional networks in Alzheimer's disease is more random. The modelling results suggest that highly connected neural network 'hubs' may be especially at risk in Alzheimer's disease.

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Encoding and retention of information in memory are associated with a sustained increase in the amplitude of neuronal oscillations for up to several seconds. We reasoned that coordination of oscillatory activity over time might be important for memory and, therefore, that the amplitude modulation of oscillations may be abnormal in Alzheimer disease (AD). To test this hypothesis, we measured magnetoencephalography (MEG) during eyes-closed rest in 19 patients diagnosed with early-stage AD and 16 agematched control subjects and characterized the autocorrelation structure of ongoing oscillations using detrended fluctuation analysis and an analysis of the life-and waiting-time statistics of oscillation bursts. We found that Alzheimer's patients had a strongly reduced incidence of alpha-band oscillation bursts with long life-or waiting-times (< 1 s) over temporo-parietal regions and markedly weaker autocorrelations on long time scales (1-25 seconds). Interestingly, the life-and waiting-times of theta oscillations over medial prefrontal regions were greatly increased. Whereas both temporo-parietal alpha and medial prefrontal theta oscillations are associated with retrieval and retention of information, metabolic and structural deficits in early-stage AD are observed primarily in temporo-parietal areas, suggesting that the enhanced oscillations in medial prefrontal cortex reflect a compensatory mechanism. Together, our results suggest that amplitude modulation of neuronal oscillations is important for cognition and that indices of amplitude dynamics of oscillations may prove useful as neuroimaging biomarkers of early-stage AD.Alzheimer's disease ͉ magnetoencephalography ͉ neuronal oscillations ͉ resting-state brain activity ͉ temporal correlations P sychological and neuroimaging data suggest that the brain performs many important functions during rest, such as retrieval and manipulation of information in short-term memory, and problem-solving and planning (1, 2). These resting-state functions may represent an essential aspect of human selfawareness and are susceptible to impairment in brain-related disorders including depression, schizophrenia, and dementia (3).Neuroimaging has identified anatomical patterns of activity that are remarkably consistent across resting-state experiments, most notably in the precuneus, lateral parietal and medial prefrontal cortices (4, 5). The existence of such a ''resting-state network'' has been suggested to reflect a ''default mode'' of brain operation in the absence of goal-directed behavior (6). Coordination of anatomically distributed activity during rest has been studied extensively by computing correlations between neuronal signals from different brain areas (Fig. 1). This approach has revealed aberrant resting-state networks in Alzheimer disease (AD) (7-9) and other disorders (4, 10, 11).For cognitive processing, coordination of local brain activity over time may be just as important as the coordination of simultaneous activity in anatomically distinct brain regions and may be refl...

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To find cortical correlates of face recognition, we manipulated the recognizability of face images in a parametric manner by masking them with narrow-band spatial noise. Face recognition performance was best at the lowest and highest noise spatial frequencies (NSFs, 2 and 45 c/image, respectively), and degraded gradually towards central NSFs (11-16 c/image). The strength of the 130-180 ms neuromagnetic response (M170) in the temporo-occipital cortex paralleled the recognition performance, whereas the mid-occipital response at 70-120 ms acted in the opposite manner, being strongest for the central NSFs. To noise stimuli without faces, M170 was small and rather insensitive to NSF, whereas the mid-occipital responses resembled closely the responses to the combined face and noise stimuli. These results suggest that the 100 ms mid-occipital response is sensitive to the central spatial frequencies that are critical for face recognition, whereas the M170 response is sensitive to the visibility of a face and closely related to face recognition.

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